Advanced Search
MyIDEAS: Login

Forecasting Equity Premia using Bayesian Dynamic Model Averaging

Contents:

Author Info

  • Joscha Beckmann
  • Rainer Schüssler

Abstract

This paper introduces a Bayesian version for Dynamic Model Averaging for predicting aggregate stock returns. Our suggested approach simultaneously accounts for many sources of uncertainty. It is designed to handle (i) parameter instability, (ii) time-varying volatility, (iii) model uncertainty and (iv) time-varying model weights. We use our method to analyze predictability of S&P500 returns for the 1927 - 2012 period. The flexibility of the econometric setup enables us to disentangle the multitude of effects at work when generating (point and density) forecasts. A key point of our analysis is to assess which components of forecast models pay off in terms of statistical accuracy and economic value. We document that statistical and economic evaluation metrics can be in sharp contrast. While stochastic volatility emerges to be important both in terms of density forecast accuracy and economic gains, return prediction models that use economic covariates turned out to be helpful to time the market only within very limited periods of time.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www1.wiwi.uni-muenster.de/cqe/forschung/publikationen/cqe-working-papers/CQE_WP_29_2014.pdf
File Function: Version of February, 2014
Download Restriction: no

Bibliographic Info

Paper provided by Center for Quantitative Economics (CQE), University of Muenster in its series CQE Working Papers with number 2914.

as in new window
Length: 50 pages
Date of creation: Feb 2014
Date of revision:
Handle: RePEc:cqe:wpaper:2914

Contact details of provider:
Postal: Am Stadtgraben 9, 48143 Münster, Germany
Phone: +49.(0).251.83-25041
Fax: +49.(0).251.83-25042
Email:
Web page: http://www1.wiwi.uni-muenster.de/cqe/
More information through EDIRC

Related research

Keywords: Asset allocation; Density forecasting; Model averaging;

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Ferreira, Miguel A. & Santa-Clara, Pedro, 2011. "Forecasting stock market returns: The sum of the parts is more than the whole," Journal of Financial Economics, Elsevier, vol. 100(3), pages 514-537, June.
  2. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
  3. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
  4. Henkel, Sam James & Martin, J. Spencer & Nardari, Federico, 2011. "Time-varying short-horizon predictability," Journal of Financial Economics, Elsevier, vol. 99(3), pages 560-580, March.
  5. Pesaran, M. Hashem & Pick, Andreas, 2011. "Forecast Combination Across Estimation Windows," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 307-318.
  6. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
  7. Pettenuzzo, Davide & Timmermann, Allan G & Valkanov, Rossen, 2013. "Forecasting Stock Returns under Economic Constraints," CEPR Discussion Papers 9377, C.E.P.R. Discussion Papers.
  8. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Time-varying Combinations of Predictive Densities using Nonlinear Filtering," Tinbergen Institute Discussion Papers 12-118/III, Tinbergen Institute.
  9. Gary Koop & Dimitris Korobilis, 2011. "Forecasting Inflation Using Dynamic Model Averaging," Working Papers 1119, University of Strathclyde Business School, Department of Economics.
  10. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
  11. Pettenuzzo, Davide & Timmermann, Allan, 2011. "Predictability of stock returns and asset allocation under structural breaks," Journal of Econometrics, Elsevier, vol. 164(1), pages 60-78, September.
Full references (including those not matched with items on IDEAS)

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:cqe:wpaper:2914. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Susanne Deckwitz).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.